While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can train machine learning models using the collected data. To take the high mobility of vehicles into account, we consider the delay as a learning parameter and restrict it to be less than a tolerable threshold. To satisfy this threshold, the central server accepts partially trained models, the distributed roadside units (a) perform downlink multicast beamforming to minimize global model distribution delay and (b) allocate optimal uplink radio resources to minimize local model offloading delay, and the vehicle agents conduct heterogeneous local model training. Using real-world vehicle trace datasets, we validate our FL solutions. Simulation shows that the proposed integrated FL platform is robust and outperforms baseline models. With reasonable local training episodes, it can effectively satisfy all constraints and deliver near ground truth multi-horizon velocity and vehicle-specific power predictions.

翻译：虽然隐私问题吸引了连接和自动化车辆以纳入船上联动学习(FL)解决方案,但迫切需要与各种计算动力意识学习平台进行综合车辆到无障碍通信,以使之成为现实。为此,我们提议建立一个新的机动、通信和计算意识在线FL平台,将上方车辆用作学习剂。由于现代车辆的先进特点,机载传感器可以将数据作为车辆沿其轨迹旅行的方式收集,而机载处理器可以利用所收集的数据来培训机器学习模型。为了考虑到车辆的高度机动性,我们认为延迟是一个学习参数,限制它的程度低于一个可容忍的门槛。为了达到这一门槛,中央服务器接受部分培训的模型,分布式路边单元(a) 进行下行连接多功能组合,以最大限度地减少全球模式分发延误,(b) 最佳的上传无线电资源以尽量减少当地模式的卸载延迟,而车辆代理人则进行混合的当地模式培训。我们用真实世界的车辆追踪数据集,验证我们的FL解决方案。为了达到这一门槛,中央服务器接受部分经过部分培训,分布式的分布式多功能模型能够有效地提供最新的地面预测模型。